Computer vision has been rapidly promoted, with the widespread application of convolutional neural networks (CNNs) in image classification [1]}, [2]}, semantic segmentation [3]}, and object detection [4]}, [5]}. It does, however, come with a huge demand for memory and computing resources. These computation and memory costs are incompatible with the computing capabilities of devices, particularly those with low resources, e.g., mobile phones and embedded devices. As a result, substantial research has been invested to reduce storage and computation cost. To accomplish this, a number of compression methods for efficient inference have been proposed, including network pruning [6]}, [7]}, [8]}, low-rank decomposition [9]}, [10]}, network quantization [11]}, [12]}, [13]}, and knowledge distillation [14]}. Network quantization, for example, is particularly well suited for using on embedded devices since it decreases the bit-width of network weights and activations. Binarization, a particularly aggressive kind of quantization, reduces CNN parameters and activations into 1 bit, reducing memory usage by \(32 \times \)  and calculation costs by \(58 \times \)  [11]}.
Binarized neural networks (BNNs) are employed for a wide range of applications, such as image classification [11]}, [12]}, [18]}, object detection [19]}, [20]} and point cloud recognition [21]}.
With high energy-efficiency, they are potent to be directly applied on AI chips.
However, due to the limited representation capabilities, BNNs' performance remains considerably inferior to that of their real-valued counterparts.
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